Date: Fri, 20 Dec 2019 05:00:00 -0500
<p>Open Source software such as scikit-Learn, Python, and Spark form the backbone of data science. In a two-part series, we’re covering the ins and outs of open source - and how this special type of software supports 98% of enterprise-level companies’ data science efforts.</p><p>In part 1, we’re chatting with Andreas Mueller, a core contributor of scikit-Learn aboutthe value in open source versus corporate software, and what it looks like to run and govern this type of community-written (and driven) project.</p><p>Join our Paris scikit-Learn sprint this January: <a href="https://github.com/scikit-learn/scikit-learn/wiki/Paris-scikit-learn-Sprint-of-the-Decade">https://github.com/scikit-learn/scikit-learn/wiki/Paris-scikit-learn-Sprint-of-the-Decade</a><br /><br />Andreas Mueller is a lecturer at the Data Science Institute at Columbia University and author of the O’Reilly book <a href="http://amueller.github.io/#book">“Introduction to Machine Learning with Python”,</a> describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and he has been co-maintaining it for several years. He is also a <a href="http://software-carpentry.org/">Software Carpentry</a> instructor. In the past, he worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. You can find his full cv <a href="http://amueller.github.io/cv_andreas_mueller.pdf">here</a>. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.</p>